A computational scheme which utilizes neural networks was
developed to predict properties of nano-structured materials and
optimization and control of nano-devices. Using a set of simple
algorithms to encode the structure and composition of the
material directly into numerical vectors neural network modules
can correlate these numeric inputs with a set of desired
properties. Calculated results for a series of hydrocarbons,
fluorohydrocarbons, amines, and crown ethers demonstrate average
accuracies of 0.2-8.1% with maximum deviations of 16-20% for a
broad range of thermodynamic, physical, biological (toxicity:
human and environmental) and physical-chemical characteristics
(heat capacity, enthalpy, heat of evaporation, boiling point,
density, refractive index, stability constants, etc.). A
molecular design tool based on the neural network capabilities of
formulating accurate quantitative structure-property
relationships is described. This technique, called computational
synthesis, is capable of formulating the structure and
composition of materials which will give a set of specified
properties. In other applications, this technique has been proven
useful in the reverse engineering of nano-fluidics and
nano-motors.

Research sponsored by the Division of Materials Sciences,
Office of Basic Energy Sciences, U.S. Department of Energy under
contract DE-AC05-96OR22464 with Lockheed-Martin Energy Research
Corp.